Extreme Value Analysis of Urban Air Quality using Internet of Things

##plugins.themes.academic_pro.article.sidebar##

Published Mar 1, 2019
https://doi.org/10.47164/ijngc.v10i1.153
Download
PDF
Statistic

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...
Volume 10, Issue 1, March 2019

##plugins.themes.academic_pro.article.main##

Anurag Barthwal
Computer Science and Engineering, Shiv Nadar University, Gautam Buddha Nagar, U.P., India -201314
Debopam Acharya
Computer Science and Engineering, Shiv Nadar University, Gautam Buddha Nagar, U.P., India -201314

Abstract

Extremely poor levels of air quality are often experienced in high pollutant concentration regions like Delhi. The analysis and forecasting of such extreme events is suitably performed using probability distributions. In this work, an end-to-end IoT based system has been developed to collect, visualize and analyze air pollution data in Delhi and NCR. Central fitting and extreme value distributions, Lognormal, Gumbel, Frechet and Weibull have been compared using goodness of fit criteria to select the best fit model for computation of exceedance probabilities and return periods for air pollution extremes. The exceedance probabilities and return periods have been compared to actual occurrences of extreme events. The results indicate that the Gumbel distribution based air quality model is best suited for forecasting air quality in this high pollutant concentration region.

##plugins.themes.academic_pro.article.details##

How to Cite
Anurag Barthwal, & Debopam Acharya. (2019). Extreme Value Analysis of Urban Air Quality using Internet of Things. International Journal of Next-Generation Computing, 10(1), 19–35. https://doi.org/10.47164/ijngc.v10i1.153
Crossref
Scopus
Google Scholar
Europe PMC

References

  1. AMANN, M., PUROHIT, P., BHANARKAR, A. D., BERTOK, I., BORKEN-KLEEFELD, J., COFALA, J., VARDHAN, B. H.2017. Managing future air quality in megacities: A case study for Delhi. Atmospheric Environment 161, 99 - 111.
  2. BHANARKAR, A. D., PUROHIT, P., RAFAJ, P., AMANN, M., BERTOK, I., COFALA, J., KUMAR, R. 2018. Managing future air quality in megacities: Co-benefit assessment for Delhi. Atmospheric Environment 186, 158 - 177.
  3. ZHANG, QIANG ET. AL. 2017. Transboundary health impacts of transported global air pollution and international trade. Nature Vol - 543, SP - 705, Macmillan Publishers Limited, Springer Nature.
  4. COHEN, AARON J. ET. AL. 2017. Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015. Lancet Volume 389, Issue 10082, 2017, Pages 1907-1918, ISSN 0140 - 6736,
  5. RAGGAD, B.. 2018. Stationary and Non-stationary Extreme Value Approaches for Modelling Extreme Temperature: the Case of Riyadh City, Saudi Arabia. Environ Model Assess 23: 99, Elsevier.
  6. CHEN, L., YANG, D , ZHANGDAQIN,G , WANG C. , LI, J., NGUYEN, THI-MAI-TRANG. 2018. Deep mobile traffic forecast and complementary base station clustering for C-RAN optimization. Journal of Network and Computer Applications, Elsevier Volume 121, Pages 59 - 69, ISSN 1084 - 8045.
  7. WANG, Y. AND CHEN, G. 2017. Efficient Data Gathering and Estimation for Metropolitan Air Quality Monitoring by Using Vehicular Sensor Networks. IEEE Transactions on Vehicular Technology Vol. 66, no. 8, pp. 7234-7248, Aug. 2017.
  8. ZHU, J. Y., SUN, C. AND LI, V. O. K. 2017. An Extended Spatio-Temporal Granger Causality Model for Air Quality Estimation with Heterogeneous Urban Big Data. IEEE Transactions on Big Data Vol. 3, no. 3, pp. 307 - 319, Sept. 1, 2017.
  9. MARTINS, LEILA DROPRINCHINSKI ET AL. 2017. Extreme value analysis of air pollution data and their comparison between two large urban regions of South America. Weather and Climate Extremes, Volume 18, Pages 44 - 54, ISSN 2212 - 0947 (Elsevier).
  10. ERCELEBI, SELAMET, TOROS AND HSEYIN. 2009. Extreme Value Analysis of Istanbul Air Pollution Data. CLEAN Soil, Air, Water, 37. 122 - 131(2009).10.1002/clen.200800041.
  11. KIM, J., CHU, C. AND SHIN, S. 2014. ISSAQ: An Integrated Sensing Systems for Real-Time Indoor Air Quality Monitoring. IEEE Sensors Journal, vol. 14, no. 12, pp. 4230 - 4244, Dec. 2014.
  12. BARTHWAL, A. AND ACHARYA, D. 2018. An Internet of Things System for Sensing, Analysis & Forecasting Urban Air Quality,” 2018 IEEE (CONECCT), Bangalore, 2018, pp. 1 - 6.
  13. WANG, X. MAUZERALL, D. L. 2004. Characterizing Distributions of Surface Ozone and Its Impact on Grain Production in China, Japan and South Korea: 1990 and 2020. Atmospheric Environment. 38, p. 4383 - 4402.
  14. LU, H. C. 2004. The Statistical Characters of PM10 Concentration in Taiwan Area. Atmospheric Environment. 36, p. 491 - 502.
  15. CENTRAL POLLUTION CONTROL BOARD, INDIA. 2014. http://cpcb.nic.in/National-Air-Quality-Index/.
  16. KARACA, F., ALAGHA, O., ERTURK, F. 2005. Statistical characterization of atmospheric PM10 and PM2.5 concentrations at a non-impacted suburban site of Istanbul, Turkey. Chemosphere. 59, p.1183 - 1190.
  17. SHARMA, P., KHARE, M. AND CHAKRABARTI, S.P. 1999. Application of extreme value theory for predicting violations of air quality standards for an urban road intersection. Transportation Research Part D: Transport and Environment, 4. 201 - 216.
  18. MDYUSOF, N. F. F., RAMLI, N. A., YAHAYA, A. S., SANSUDDIN, N., GHAZALI, N. A. AND AL MADHOUN, W. A. 2011. Central fitting distributions and extreme value distributions for prediction of high PM10 concentration. 2011 International Conference on Multimedia Technology, Hangzhou, 2011, pp. 6263 - 6266.
  19. KAHN, HENRY D. 1973. Note On The Distribution of Air Pollutants. Journal of the Air Pollution Control Association, 23:11, 973 - 973.